Interviews

Over 200 million data points are fed into our system to improve our prediction of traffic patterns at every corner of the city – Surajit Das, Routematic

Routematic is an Urban Mobility company focused on providing employee transportation solutions to India Inc. Routematic was launched as a employee transportation software solution in December 2013 and since then has been continuously innovating and adding to our solution portfolio.

Routematic has pioneered the technology integrated fleet solution for the employee transportation market and have established ourselves as the preferred solution in the industry.

IncubateIND team recently spoke with Surajit Das, CEO – Routematic on the future of Urban Mobility and how new technology like Artificial Intelligence and IoT are redefining urban commute.

Tell us something about yourself and what does Routematic do?

I have always enjoyed the adventure of  the hustle of solving real world problems through technology. Therefore, unlike most professionals over 30, I left a well defined corporate career in IT Services 9 years ago to start my entrepreneurial journey . The India growth story and the sense of adventure of building a company from scratch was too compelling. My first milestone after my return from the US, was to get the professional training I needed to start a venture. An MBA from ISB got me a high quality education and a strong network. Subsequently, after a year of market exploration, I started my first venture in personal financial management. The venture wasnt well timed, so didn’t succeed. Routematic is my second venture, along with two other Co-founders.

Routematic is a tech- enabled, fleet management company. We provide cab fleet to Corporates that extend office commute benefit to their employees. Our AI driven automation tech model makes our business model scalable, cost effective, transparent and reliable.

We have two revenue models. First, our SaaS product helps the corporate to manage the entire transport desk by integrating employee, cab and transport desk onto a cloud platform. Second, we provide end to end transport service that includes SaaS and Routematic fleet.

How do you think the transportation will change in the future?

There are two distinct changes I foresee in the way we commute to work. As technology matures, I think the market will catch up to this thought leading to a large influx of electric vehicles in the not so distant future.

Second big change is the people’s mindset towards commute. Historically, employees always wanted to commute to work in their own respective vehicles. I see a shift in pattern where thanks to the advent of many cab aggregators, the working class is now beginning to use commercial transport for commute. It is evident that booking a cab to work today is a matter of convenience over cost. Today’s employee understands that she can spend the commute time doing something more productive rather than driving. The only problem to solve is the aspect of predictability and affordability of the commute.

How artificial intelligence is redefining urban commute?

AI has a variety of applications to improve safety and security within urban mobility.

At a supervised learning level, AI is used to improve security by ensuring that only approved drivers can take the trip. This is done through driver facial recognition system. This is to ensure that only the driver who is authorised, can honour the trip. Very often, we see that the person driving the cab is not the driver whose background check and documentation has been done. Therefore, this can now be done before every trip is executed. AI is also used to improve trip safety by monitoring the drivers ability to handle the car. The mobile phone has several sensors like Gyroscope and accelerometers that can help in this regard. It is just a matter of training the system to identify harsh braking, over speeding, sharp u turns or going too fast on a speed breaker

The world of AI gets really exciting at an unsupervised level. The entire science of optimizing commute is about understanding how traffic interacts between work zones and the rest of the city. A demand and supply modeling for cabs to predict traffic situation and expected time of arrivals at a later date/time is the ultimate objective of Reinforced learning for optimality.

Everyday, over 200 million data points are fed into our system to improve our prediction of traffic patterns at every corner of the city and we have been doing this for 5 years. The success in enterprise mobility is highly correlated to the firm’s ability to ensure that the employees reach office on time. The challenge is to be able to accurately predict the ETA (Expected Time of Arrival) while being able to club the pickup of all 4/6 employees in a cab. This data dividend will now begun to pay off as we use AI to predict fleet route patterns and expected demand at different times of the day using this data.   The On Time Arrival percentage in urban commute is 60%. We believe that this can be significantly improved using AI, as prediction systems gets more efficient.

IoT will have a big impact on urban commute. What are your thoughts on this?

IOT has become a necessity in Corporate Fleet services. We design for traffic to get you data. The data helps in redesigning the existing design.  Today, the largest fleet operators still work on paper & phone based processes and rely heavily on human intervention for basic decision making. IOT changes everything. IOT is mainly used in 2 workflows:

  • Full visibility to all stakeholders – Much like Uber, an employee that has booked a corporate cab, needs to know the current location so that s/he can reach the pick up point on time. This is executed by integrating both the driver and employee mobiles through apps. The transport desk can also track the vehicle status because of IOT.
  • Fleet Management- Corporate fleet management requires coordinating with multiple drivers, sometimes over 1000 vehicles in the city and assigning trips to them for either pick up or drop of a corporate employee based on their current location and the likelihood of delivering on an assigned trip. Most fleet operators manage this through phone calls. IOT based systems can use the data available to assess if a particular driver may not be able to make the trip. If not, reassign the trip to another driver.

How will big data make transportation more efficient and effective?

Big data is used to create heat maps for traffic predictions and demand and supply predictions for cab services. The process is simple. A demand and supply heat map is created based on past data, which makes predictions and moves cab fleet closer to the potential demand locations for that time period. Once time period has lapsed, more information is fed to further optimize the design thereby making it more efficient.

 

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